Ridge Estimator for the Bell-Touchard Regression Model
Ridge Estimator; Bell-Touchard Distribution; Multicollinearity.
The two-parametric discrete distribution family known as the Bell-Touchard
distribution has been recently proposed in the statistical literature. It can be used to model
count data and stands out for its flexibility, especially in scenarios of overdispersion, where the
variance exceeds the mean. The regression model for response variables in count form based
on this distribution has also been presented in the literature. The Ridge estimator is widely
used to address multicollinearity in regression models, employing an approach that
incorporates a penalty on the regression coefficients, thereby reducing prediction errors. In
this work, the Ridge estimator will be applied to the Bell-Touchard regression model with the
aim of modeling count data in the presence of multicollinearity. Monte Carlo simulations
comparing the Ridge estimator with the traditional maximum likelihood estimator will be
presented and discussed. Finally, applications to real data will be considered.